Agenda

Master thesis defence

Deep Learning-based identification of human gait by radar micro-Doppler measurements

Vasileios Papanastasiuo

The radar micro-Doppler (m-D) signature of human gait has already been used successfully for a few classification tasks of human gait, for instance walking versus running and determining the number of humans under observation. How ever, the more challenging problem of personnel identification has not been solved yet. The aim of this study is to prove that the human walking gait differs between individuals and that it can be used for personnel identification using CW X-band radar measurements. This study investigates the effect of human walking gait characteristics such as speed and stride as well as the gender on leading to distinctive m-D signatures. Both simulated data and measurements of 22 subjects walking from and towards the radar were used. Unsupervised learning based on Adversarial Autoencoders was used to map the m-D signatures to a latent space. TDistributed Stochastic Neighbor Embedding and Uniform Manifold Approximation and Projection were then used for clustering and visualization. This study shows that even very slight changes in the walking gait characteristics mentioned above lead to distinctive m-D signatures mapped into closely located points in the latent space. A VGG-16 convolutional neural network was used to identify the walking subjects based on their measured m-D signature. Accuracy of above 93.5% was achieved, proving that CW X-band radar m-D signature of human walking gait can be used for accurate personnel identification which is reliable for 22 participants.

Overview of MSc ME Thesis Presentation

Agenda

MSc TC Thesis Presentation

Erkut Yiğit

MSc ME Thesis Presentation

Jeroen Naaborg

High density integrated capacitors for smart catheters and implants

MSc ME Thesis Presentation

Aoibhinn Larkin Reddington

Noninvasive Hemodynamic Monitoring: Left Ventricular Pressure-Volume Loop Reconstruction

MSc SS Thesis Presentation

Bram Visser

Calibration of single element 3D ultrasound with a aberration mask

Improved calibration by modeling non-linear frequency scaling and semi-linear phase shifts

Master thesis defence

Ahmed, Sheeraz

Multi-Channel Waveform Agile Radar: Experimental performance evaluation of Advanced Space-Time Adaptive Processing (ASTAP) radar system

Master thesis defence

Min DIng

Model-based Interference Mitigation for FMCW Radar System

Master thesis defence

Prithvi Laguduvan Thyagarajan

STEREOID data processor: Design and Performance analysis

Master thesis defence

Nick Cancrinus

Multiple-input Multiple-output Grating Lobe Selection Scheme for Radar Applications

Master thesis defence

Guigeng Su

Detection of vital signs of auto driver and passengers using (distributed) radars inside auto

Master thesis defence

Jiadi Zhang

Super-resolution Algorithm for Target Localization using Multiple FMCW Automotive Radars

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